Conditions for reliable dewatering systems based on voidless drainage in slightly permeable soils

Conclusions 1. Use of dewatering systems based on voidless drainage is most effective when dewatering slightly permeable soils in regions with a deep seasonal frost. 2. Two-story drainage with the upper and lower stories spaced 30 m apart accelerates the discharge of gravitational water from the arable layer. Results of observations on two-story drainage in experimental-production sections indicated that it discharges 2 – 3.8 times more water than systematic tubular drainage with an interdrain spacing of 10 m and a 0.2-m thick three-dimensional filter of a sandy-gravelly mixture. 3. It is recommended to use crushed stone and porous-clay gravel with a fineness of 10 – 20 mm as a filler. In that case, protection of the drain filler is, as a rule, required only from the intrusion of soil from above during construction in cohesive soils with a plasticity indexW _p≥7. 4. seepage rate in a voidless drain should be lower than the scouring velocity for the soil in which the voidless drainage is placed. In the opposite case, it is necessary to lower the grade of the drains or select a filler gradation such that the seepage rate in the drain be lower than the scouring velocity. Translated from Gidrotekhnicheskoe Stroitel’stvo, No. 8, pp. 41–43, August, 1998.     

Performance evaluation of rule grouping on a real-time expertsystem architecture

Uses a Markov process to model a real-time expert system architecture characterized by message passing and event-driven scheduling. The model is applied to the performance evaluation of rule grouping for real-time expert systems running on this architecture. An optimizing algorithm based on Kernighan-Lin heuristic graph partitioning for the real-time architecture is developed and a demonstration system based on the model and algorithm has been developed and tested on a portion of the advanced GPS receiver (AGR) and manned manoeuvring unit (MMU) knowledge bases     

Quantization effects in digitally behaving circuit implementationsof Kohonen networks

Implementing a neural network on a digital or mixed analog and digital chip yields the quantization of the synaptic weights dynamics. This paper addresses this topic in the case of Kohonen's self-organizing maps. We first study qualitatively how the quantization affects the convergence and the properties, and deduce from this analysis the way to choose the parameters of the network (adaptation gain and neighborhood). We show that a spatially decreasing neighborhood function is far more preferable than the usually rectangular neighborhood function, because of the weight quantization. Based on these results, an analog nonlinear network, integrated in a standard CMOS technology, and implementing this spatially decreasing neighborhood function is then presented. It can be used in a mixed analog and digital circuit implementation.     

Genetic evolution of the topology and weight distribution of neuralnetworks

This paper proposes a system based on a parallel genetic algorithm with enhanced encoding and operational abilities. The system, used to evolve feedforward artificial neural networks, has been applied to two widely different problem areas: Boolean function learning and robot control. It is shown that the good results obtained in both cases are due to two factors: first, the enhanced exploration abilities provided by the search-space reducing evolution of both coding granularity and network topology, and, second, the enhanced exploitational abilities due to a recently proposed cooperative local optimizing genetic operator.     

Large-deviation inequalities for parameter estimators in stochastic systems

Translated from Kibernetika i Sistemnyi Analiz, No. 2, pp. 95–112, March–April, 1994.     

Algebra of algorithms and Kaluzhnin's graphschemas

This research was supported under project No. 6.02.02/128-93 as part of the state scientific-technical program on future information technologies and systems by the Ukrainian State Committee, of Science and Technology.